How To Get Hired As A Data Scientist

McKinsey Global Institute projects the need in the US alone for data science jobs, will be between 140,000 and 180,000. It is no wonder that data science is being touted as the hottest career option of the 21st Century. And following the suit is India where the need for data scientists is definitely on an upward trend.

According to market predictions, the big data market by 2021, will grow from $28.65 Billion in 2016 to $66.79 Billion (at a Compound Annual Growth Rate (CAGR) of 18% ). The explosion of mobile devices and apps, and the burgeoning data available has paved the way for the need for big data analytics and solutions across many organizations. This, in turn, has increased the demand for data scientists all over the world. A demand that is unlikely to die down soon.

As it is with any new field, the hype factor is high. Now, tech professionals looking for a job in big data are suddenly somehow ‘data scientists’! Through this article, we articulate how mid-career tech professionals considering a career in big data can upskill themselves and look at a prosperous career in data science. This shifts the focus on techies among their mid-career techies since data scientists are the new professionals who can unlock the potential of an increasingly data-rich world.

To start with, the heart of someone who is eyeing a career in data science should be with data. Not only should the person be good at comprehending the data sets, but also should be able to visualize the outcomes and benefits of data as a first-class product. The ability to churn chunks of data into meaningful observations should be the prerequisite for any data scientist.

Secondly, they are masters of programming and statistics, together with high domain knowledge. To become a successful data scientist, they need to be good at several subject disciplines.

And lastly, technical know-how shouldn't act as a barrier for and aspiring data-scientist. Creativity sits at the heart of every data scientist, as they find newer, unexplored methods and ways to visualize information from data sets.

A good data scientist (a powerful and rare combination of)

= data hacker + programmer + analyst + coach + story teller + artist

The road to data science

The path to becoming a successful data scientist lies in the science behind the domain of data. So, let’s start with what it takes to be in it. An ideal data scientist should be able to gather, collect, explore, group and represent data of all forms and combinations. The hard truth, however, is that there are a limited number of people who possess the skills to be an ideal data scientist.

To begin with, someone who is skilled with the ability to write a code will eventually become a data scientist. Possessing competence in programming helps in the path of becoming a great data scientist. You then must identify the gaps in your skills, whether it is programming, statistics or domain related.

Here’s a tip: Assess your shortcomings, and then try to work with those who possess those skills. You should pitch to get projects with a ‘mentor’, where you can get your hands dirty with data. Next, you should get on an accelerated path and explore data beyond your comfort zone. And learn about new technologies and methods as well.

Data scientist jobs

With more connected devices and the advent of the Internet of Things, every industry is seeing the demand for data scientists today. However, industries such as hospitality, telecom, healthcare and financial services are seeing the highest demand as of today. On the other hand, industries dependent on data, such as ad tech, will likely to have a continuous hiring demand for such skilled professionals.

A potential checklist employers use

Can the said candidate program. Being the best programmer is not the prerequisite here, however, the ability to quickly prototype algorithms is something that should be looked for.

Can a candidate visualize the outcome of interpreting the data, and efficiently communicate the same findings - both visually and verbally?

Do they understand the current businesses nuances and challenges of the working environment and how quickly can they deploy data analytics to solve issues?

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